Browse Items (20 total)

In molecular biology, advances in high-throughput technologies have made it possible to study complex multivariate phenotypes and their simultaneous associations with
high-dimensional genomic and other omics data, a problem that can be studied…

In this study, we present a new module built for users interested in a programming
language similar to BUGS to fit a Bayesian model based on the piecewise exponential (PE)
distribution. The module is an extension to the open-source program JAGS by…

Traditional regression models, including generalized linear mixed models, focus on understanding the deterministic factors that affect the mean of a response variable. Many
biological studies seek to understand non-deterministic patterns in the…

We introduce the new package dmbc that implements a Bayesian algorithm for clustering a set of binary dissimilarity matrices within a model-based framework. Specifically, we
consider the case when S matrices are available, each describing the…

Use of historical data in clinical trial design and analysis has shown various advantages such as reduction of number of subjects and increase of study power. The metaanalytic-predictive (MAP) approach accounts with a hierarchical model for…

ABCpy is a highly modular scientific library for approximate Bayesian computation
(ABC) written in Python. The main contribution of this paper is to document a software
engineering effort that enables domain scientists to easily apply ABC to their…

BNPmix is an R package for Bayesian nonparametric multivariate density estimation, clustering, and regression, using Pitman-Yor mixture models, a flexible and robust
generalization of the popular class of Dirichlet process mixture models. A variety…

Booming in business and a staple analysis in medical trials, the A/B test assesses
the effect of an intervention or treatment by comparing its success rate with that of a
control condition. Across many practical applications, it is desirable that…

Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting
in TVP models is well known. This issue can be dealt with using…

Item response theory (IRT) is widely applied in the human sciences to model persons’
responses on a set of items measuring one or more latent constructs. While several
R packages have been developed that implement IRT models, they tend to be…